Neural Network Pruning Rate Analysis for Reliable Data Processing
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Solution Overview
Problem
Existing neural network pruning methods lead to unreliable data processing performance and inefficient resource utilization due to varying network processing performance after pruning, particularly in mobile terminals with limited computing resources.
Innovation Solution
A method involving pruning processes on candidate network layers at varying rates to generate sub-neural networks, analyzing parameter redundancies based on performance losses, and selecting optimal pruning rates to ensure precision and efficiency, utilizing different pruners for weight distributions and considering channel dependencies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by moving object
If pruning processes are performed on candidate network layers to reduce model size and computing resources, then resource consumption is reduced, but data processing performance becomes unreliable and varies
Solution Approach 1:
The patent applies parameter changes by systematically varying pruning rates across different candidate network layers and analyzing the resulting performance metrics. This involves changing the pruning parameter (retention ratio) to identify optimal values that balance resource reduction with performance reliability. The method evaluates multiple pruning rates and selects the optimal configuration based on performance analysis, thereby resolving the contradiction between resource reduction and performance reliability.
Solution Approach 2:
The patent implements feedback mechanisms by evaluating the performance of pruned network layers using test datasets and analyzing performance losses. The system provides feedback on how different pruning rates affect overall network performance, allowing iterative optimization. This feedback loop enables the identification of pruning strategies that maintain reliable data processing performance while reducing computing resources.
2Device complexity
If aggressive pruning is applied to reduce model complexity, then device complexity is reduced, but manufacturing precision of the neural network model deteriorates
Solution Approach 1:
The patent systematically changes the pruning rate parameter to find the optimal balance between model complexity and precision. By evaluating multiple pruning rates and analyzing performance losses, the method identifies the threshold where further pruning would significantly degrade model precision. This parameter optimization approach allows aggressive pruning up to the point of diminishing returns, thereby reducing complexity while maintaining acceptable precision.
Solution Approach 2:
The patent applies partial pruning strategies by selectively pruning only certain network layers or channels rather than uniformly pruning the entire network. This partial action approach allows the model to maintain precision in critical layers while reducing complexity in less important layers. The method evaluates the impact of pruning on different parts of the network and applies pruning selectively, achieving complexity reduction without excessive loss of precision.
3Loss of substance
If pruning rates are increased to achieve better compression, then network compression degree increases, but performance loss increases
Solution Approach 1:
The patent optimizes the pruning rate parameter to achieve the best compression-to-performance ratio. By systematically varying pruning rates and analyzing performance losses, the method identifies the optimal compression level where further pruning would cause excessive performance degradation. This parameter optimization enables achieving significant network compression while minimizing information loss.
Solution Approach 2:
The patent performs preliminary evaluation of candidate network layers to identify which layers are most suitable for pruning before actually applying compression. By analyzing layer importance and potential impact on performance in advance, the method prepares a pruning strategy that maximizes compression while minimizing performance loss. This preliminary action allows the system to achieve better compression ratios without excessive performance degradation.
Data Source
AI summary
Embodiments of the present disclosure relate to a data processing method and apparatus, a device, and a medium. The method comprises: respectively performing pruning processing on candidate network layers in an original neural network according to a plurality of preset pruning rates to obtain a plurality of corresponding sub-neural networks; respectively inputting test data sets into the original neural network and the plurality of sub-neural networks for processing, and obtaining, on the basis of output data sets of the original neural network and the plurality of sub-neural networks, a reference performance index corresponding to the original neural network and a plurality of test performance indexes corresponding to the plurality of sub-neural networks; and analyzing, according to performance losses of the plurality of test performance indexes relative to a reference performance index, parameter redundancies of parameters of the candidate network layers in the original neural network under different pruning rates.